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8aab39e3ce
# Added SmartGPT workflow by providing SmartLLM wrapper around LLMs
Edit:
As @hwchase17 suggested, this should be a chain, not an LLM. I have
adapted the PR.
It is used like this:
```
from langchain.prompts import PromptTemplate
from langchain.chains import SmartLLMChain
from langchain.chat_models import ChatOpenAI
hard_question = "I have a 12 liter jug and a 6 liter jug. I want to measure 6 liters. How do I do it?"
hard_question_prompt = PromptTemplate.from_template(hard_question)
llm = ChatOpenAI(model_name="gpt-4")
prompt = PromptTemplate.from_template(hard_question)
chain = SmartLLMChain(llm=llm, prompt=prompt, verbose=True)
chain.run({})
```
Original text:
Added SmartLLM wrapper around LLMs to allow for SmartGPT workflow (as in
https://youtu.be/wVzuvf9D9BU). SmartLLM can be used wherever LLM can be
used. E.g:
```
smart_llm = SmartLLM(llm=OpenAI())
smart_llm("What would be a good company name for a company that makes colorful socks?")
```
or
```
smart_llm = SmartLLM(llm=OpenAI())
prompt = PromptTemplate(
input_variables=["product"],
template="What is a good name for a company that makes {product}?",
)
chain = LLMChain(llm=smart_llm, prompt=prompt)
chain.run("colorful socks")
```
SmartGPT consists of 3 steps:
1. Ideate - generate n possible solutions ("ideas") to user prompt
2. Critique - find flaws in every idea & select best one
3. Resolve - improve upon best idea & return it
Fixes #4463
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
- @hwchase17
- @agola11
Twitter: [@UmerHAdil](https://twitter.com/@UmerHAdil) | Discord:
RicChilligerDude#7589
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
121 lines
4.4 KiB
Python
121 lines
4.4 KiB
Python
"""Test SmartLLM."""
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from langchain.chat_models import FakeListChatModel
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from langchain.llms import FakeListLLM
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from langchain.prompts.prompt import PromptTemplate
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from langchain_experimental.smart_llm import SmartLLMChain
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def test_ideation() -> None:
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# test that correct responses are returned
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responses = ["Idea 1", "Idea 2", "Idea 3"]
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llm = FakeListLLM(responses=responses)
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prompt = PromptTemplate(
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input_variables=["product"],
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template="What is a good name for a company that makes {product}?",
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)
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chain = SmartLLMChain(llm=llm, prompt=prompt)
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prompt_value, _ = chain.prep_prompts({"product": "socks"})
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chain.history.question = prompt_value.to_string()
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results = chain._ideate()
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assert results == responses
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# test that correct number of responses are returned
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for i in range(1, 5):
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responses = [f"Idea {j+1}" for j in range(i)]
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llm = FakeListLLM(responses=responses)
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chain = SmartLLMChain(llm=llm, prompt=prompt, n_ideas=i)
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prompt_value, _ = chain.prep_prompts({"product": "socks"})
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chain.history.question = prompt_value.to_string()
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results = chain._ideate()
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assert len(results) == i
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def test_critique() -> None:
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response = "Test Critique"
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llm = FakeListLLM(responses=[response])
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prompt = PromptTemplate(
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input_variables=["product"],
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template="What is a good name for a company that makes {product}?",
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)
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chain = SmartLLMChain(llm=llm, prompt=prompt, n_ideas=2)
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prompt_value, _ = chain.prep_prompts({"product": "socks"})
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chain.history.question = prompt_value.to_string()
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chain.history.ideas = ["Test Idea 1", "Test Idea 2"]
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result = chain._critique()
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assert result == response
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def test_resolver() -> None:
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response = "Test resolution"
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llm = FakeListLLM(responses=[response])
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prompt = PromptTemplate(
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input_variables=["product"],
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template="What is a good name for a company that makes {product}?",
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)
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chain = SmartLLMChain(llm=llm, prompt=prompt, n_ideas=2)
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prompt_value, _ = chain.prep_prompts({"product": "socks"})
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chain.history.question = prompt_value.to_string()
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chain.history.ideas = ["Test Idea 1", "Test Idea 2"]
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chain.history.critique = "Test Critique"
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result = chain._resolve()
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assert result == response
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def test_all_steps() -> None:
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joke = "Why did the chicken cross the Mobius strip?"
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response = "Resolution response"
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ideation_llm = FakeListLLM(responses=["Ideation response" for _ in range(20)])
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critique_llm = FakeListLLM(responses=["Critique response" for _ in range(20)])
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resolver_llm = FakeListLLM(responses=[response for _ in range(20)])
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prompt = PromptTemplate(
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input_variables=["joke"],
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template="Explain this joke to me: {joke}?",
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)
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chain = SmartLLMChain(
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ideation_llm=ideation_llm,
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critique_llm=critique_llm,
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resolver_llm=resolver_llm,
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prompt=prompt,
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)
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result = chain(joke)
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assert result["joke"] == joke
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assert result["resolution"] == response
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def test_intermediate_output() -> None:
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joke = "Why did the chicken cross the Mobius strip?"
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llm = FakeListLLM(responses=[f"Response {i+1}" for i in range(5)])
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prompt = PromptTemplate(
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input_variables=["joke"],
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template="Explain this joke to me: {joke}?",
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)
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chain = SmartLLMChain(llm=llm, prompt=prompt, return_intermediate_steps=True)
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result = chain(joke)
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assert result["joke"] == joke
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assert result["ideas"] == [f"Response {i+1}" for i in range(3)]
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assert result["critique"] == "Response 4"
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assert result["resolution"] == "Response 5"
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def test_all_steps_with_chat_model() -> None:
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joke = "Why did the chicken cross the Mobius strip?"
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response = "Resolution response"
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ideation_llm = FakeListChatModel(responses=["Ideation response" for _ in range(20)])
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critique_llm = FakeListChatModel(responses=["Critique response" for _ in range(20)])
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resolver_llm = FakeListChatModel(responses=[response for _ in range(20)])
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prompt = PromptTemplate(
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input_variables=["joke"],
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template="Explain this joke to me: {joke}?",
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)
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chain = SmartLLMChain(
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ideation_llm=ideation_llm,
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critique_llm=critique_llm,
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resolver_llm=resolver_llm,
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prompt=prompt,
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)
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result = chain(joke)
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assert result["joke"] == joke
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assert result["resolution"] == response
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